1. Field
The present application generally relates to optical metrology, and, more particularly, to evaluating and enhancing a library generated using a machine learning system.
2. Description of the Related Art
In semiconductor manufacturing, periodic gratings are typically used for quality assurance. For example, one typical use of periodic gratings includes fabricating a periodic grating in proximity to the operating structure of a semiconductor chip. The periodic grating is then illuminated with an electromagnetic radiation. The electromagnetic radiation that deflects off of the periodic grating are collected as a diffraction signal. The diffraction signal is then analyzed to determine whether the periodic grating, and by extension whether the operating structure of the semiconductor chip, has been fabricated according to specifications.
In one conventional system, the diffraction signal collected from illuminating the periodic grating (the measured-diffraction signal) is compared to a library of simulated-diffraction signals. Each simulated-diffraction signal in the library is associated with a hypothetical profile. When a match is made between the measured-diffraction signal and one of the simulated-diffraction signals in the library, the hypothetical profile associated with the simulated-diffraction signal is presumed to represent the actual profile of the periodic grating.
The library of simulated-diffraction signals can be generated using rigorous method, such as rigorous coupled wave analysis (RCWA). More particularly, in the diffraction modeling technique, a simulated-diffraction signal is calculated based, in part, on solving Maxwell's equations. Calculating the simulated diffraction signal involves performing a large number of complex calculations, which can be time consuming and costly.
An alternative is to generate the library of simulated-diffraction signals using a machine learning system (MLS). Prior to generating the library of simulated-diffraction signals, the MLS is trained using know input and output data. For a library generated using MLS, it is desirable to evaluate the accuracy of the trained system, especially near the boundaries of the library. In particular, as the critical dimension (CD) measured using metrology decreases, it is desirable to increase the accuracy of the library. Additionally, it is desirable to minimize the amounts of information stored in the library.